The invention relates generally to process management systems, and more particularly to scheduling systems in the clinical setting, such as healthcare delivery institutions or hospitals.
Healthcare delivery institutions are business systems that can be designed and operated to achieve their stated missions robustly. As is the case with other business systems such as those designed to provide services and manufactured goods, there are benefits to managing variation such that the stake-holders within these business systems can focus more fully on the value added core processes that achieve the stated mission and less on activity responding to variations such as emergency procedures, regular medical interventions, delays, accelerations, backups, underutilized assets, unplanned overtime by staff and stock outs of material, equipment, people and space that are impacted in the course of delivering healthcare.
Currently clinical process decisions have historically relied on the art of understanding symptoms and diagnosing causality much in alignment with the practice of the medical diagnosis arts. In an ever-evolving environment, judgment and experientially-developed mental models are utilized by the healthcare providers to utilize the information currently at hand to make decisions. Presented with similar data, the decision made from one caregiver to another typically exhibits a variation. Presented with partial information, which is the byproduct of being organized in functional departments, specialties, roles and by the nature of having partial and/or current or dated information availability on hand—clinical process decisions vary widely and typically are locally focused for lack of a systems view upstream and downstream of the decision point.
As a hospital processes care plans on an increasing patient load, these variations in medical condition and selected treatment plans perturbs the schedules of doctors, nurses and assets such as rooms and equipment. If there is protective capacity in these schedules and staff, the providers of care can manage variation while maintaining care quality. When randomness and interdependencies exceed the ability to serve, care providers are forced to make choices amongst poor alternative options; someone or something is going to be bottlenecked or overextended. Delays, queues, overtime, burnout and emotional decision making characterize systems that are over-taxed or beyond their ability to perform.
Where information systems exist, they are simply informational in nature. Examples include scheduled rooms, people, materials and equipment. Recent advances in locating devices such as those utilizing radio-frequency identification (RFID) technology to report a location of a tagged asset are utilized, yet personnel are loath to be tracked by wearing a device. RFID devices are not pervasive, and the systems that monitor them are not fully integrated with the requisite adjacent systems that gather contextual information. The current art is not predictive, probabilistic nor necessarily systemic. For example, knowing the location of an asset is desirable but knowing its anticipated need and interdependencies is required to make a decision to use a located asset actionable. The information required for such a decision comes from a plurality of adjacent health information systems and must have an ability to play forward into the future.
Today, current procedure duration and room status is provided without any proactive or forward-looking capability. Schedules are produced before a day's activities commence. Process status is displayed along with trending and, often, alarm functionality should a process variable trip a threshold set point. Today, processes are planned for a given volume; when that volume is exceeded or processes have sufficient variation to overtax their capability, scheduling and recovery are reduced to manual triage and experience to sort out. Typically, queues, delay, overtime and cancellation result; there is no proactive decision support to dynamically reschedule people or physical assets or supplies.
Radiology Information Systems (RIS) and other clinical information systems are in wide use in the healthcare industry to manage radiology departments in hospitals and independent radiology clinics. These systems typically incorporate functionality to schedule patients on radiology equipment such as computed tomography (CT) and magnetic resonance imaging (MRI) machines. However, radiology exams also require a numbers of other resources such as technicians, nurses, radiologists, anesthesiologists and other equipment such as portable ultra sound and X-ray machines. In general, these resources are not scheduled and are assumed to be available during the times when the exams are scheduled. However, this is not always true and leads to delays in completing the scheduled exams.